Prediction Model for Time Series Data Based on Multivariate Standardized Regression

This paper conducts an in-depth analysis of the impact of various factors on outcomes using time series data, focusing on trend shifts in event sequences. First, the data are preprocessed, and an index system is constructed. After incorporating the momentum difference as a key feature, the paper introduces a trend shift model based on 0-1 logistic regression. The model is trained using data from 31 pairs of events, achieving a high accuracy rate of $93.5 \%$ when comparing the predicted trend shift function values with actual results. Turning points in the process are identified using predefined thresholds. In one case study, the model successfully detects 11 upper-threshold and 10 lower-threshold turning points, corresponding closely to actual trends in the data. Finally, multivariate standardized regression is applied to assess the correlation between indicators within the trend shift model. The results reveal that three factors—the rate of service points lost, the rate of breakpoint attacks, and the momentum difference—are the most significant predictors in analyzing trend shifts. This comprehensive approach provides valuable insights into identifying and forecasting trend shifts in time series data.